Open Source Python Diagram Software

ChatterBot is a Python library that makes it easy to generate automated responses to a user’s input. ChatterBot uses a selection of machine learning algorithms to produce different types of responses. This makes it easy for developers to create chat bots and automate conversations with users. For more details about the ideas and concepts behind ChatterBot see the process flow diagram. The language independent design of ChatterBot allows it to be trained to speak any language. Additionally, the machine-learning nature of ChatterBot allows an agent instance to improve it’s own knowledge of possible responses as it interacts with humans and other sources of informative data. An untrained instance of ChatterBot starts off with no knowledge of how to communicate. Each time a user enters a statement, the library saves the text that they entered and the text that the statement was in response to. As ChatterBot receives more input the number of responses that it can reply increase.

Diagrams lets you draw the cloud system architecture in Python code. It was born for prototyping a new system architecture without any design tools. You can also describe or visualize the existing system architecture as well. Diagram as Code allows you to track the architecture diagram changes in any version control system. Diagrams currently support main major providers including AWS, Azure, GCP, Kubernetes, Alibaba Cloud, Oracle Cloud, etc. It also supports On-Premise nodes, SaaS and major Programming frameworks and languages. It does not control any actual cloud resources nor does it generate cloud formation or terraform code. It is just for drawing the cloud system architecture diagrams.

This library contains 9 modules, each of which can be used independently within your existing codebase, or combined together for a complete train/test workflow. To compute the loss in your training loop, pass in the embeddings computed by your model, and the corresponding labels. The embeddings should have size (N, embedding_size), and the labels should have size (N), where N is the batch size. The TripletMarginLoss computes all possible triplets within the batch, based on the labels you pass into it. Anchor-positive pairs are formed by embeddings that share the same label, and anchor-negative pairs are formed by embeddings that have different labels. Loss functions can be customized using distances, reducers, and regularizers. In the diagram below, a miner finds the indices of hard pairs within a batch. These are used to index into the distance matrix, computed by the distance object. For this diagram, the loss function is pair-based, so it computes a loss per pair.

This is intended as an open-source, multi-platform link diagramming tool for intelligence analysis. The goal is to be able to store objects using any database abstraction or in XML. This project needs much wheel reinvention, so assistance is welcome.

Round Trip Engineering UML Class diagram tool. No longer being maintained.

Perform a merge between two branches incrementally. If conflicts are encountered, figure out exactly which pairs of commits conflict, and present the user with one pairwise conflict at a time for resolution. Reduce the pain of resolving merge conflicts to its unavoidable minimum, by finding and presenting the smallest possible conflicts: those between the changes introduced by one commit from each branch. Allow a merge to be saved, tested, interrupted, published, and collaborated on while it is in progress. Multiple incremental merges can be in progress at the same time. Each incremental merge has a name, and its progress is recorded in the Git repository as references under refs/imerge/NAME. The current state of an incremental merge can be visualized using the diagram command. An incremental merge can be interrupted and resumed arbitrarily, or even pushed to a server to allow somebody else to work on it.